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Health trajectories in older patients hospitalized for COVID-19: Results from the GeroCovid multicenter study

Published:December 09, 2022DOI:https://doi.org/10.1016/j.rmed.2022.107088

      Abstract

      Background

      COVID-19 has disproportionately affected older adults. Yet, healthcare trajectories experienced by older persons hospitalized for COVID-19 have not been investigated. This study aimed at estimating the probabilities of transitions between severity states in older adults admitted in COVID-19 acute wards and at identifying the factors associated with such dynamics.

      Methods

      COVID-19 patients aged ≥60 years hospitalized between March and December 2020 were involved in the multicentre GeroCovid project–acute wards substudy. Sociodemographic and health data were obtained from medical records. Clinical states during hospitalization were categorized on a seven-category scale, ranging from hospital discharge to death. Based on the transitions between these states, first, we defined patients’ clinical course as positive (only improvements), negative (only worsening), or fluctuating (both improvements and worsening). Second, we focused on the single transitions between clinical states and estimated their probability (through multistage Markov modeling) and associated factors (with proportional intensity models).

      Results

      Of the 1024 included patients (mean age 78.1 years, 51.1% women), 637 (62.2%) had a positive, 66 (6.4%) had a fluctuating, and 321 (31.3%) had a negative clinical course. Patients with a fluctuating clinical course were younger, had better mobility and cognitive levels, fewer diseases, but a higher prevalence of cardiovascular disease and obesity. Considering the single transitions, the probability that older COVID-19 patients experienced clinical changes was higher within a 10-day timeframe, especially for milder clinical states. Older age, male sex, lower mobility level, multimorbidity, and hospitalization during the COVID-19 first wave (compared with the second one) were associated with an increased probability of progressing towards worse clinical states or with a lower recovery.

      Conclusion

      COVID-19 in older inpatients has a complex and dynamic clinical course. Identifying individuals more likely to experience a fluctuating clinical course and sudden worsening may help organize healthcare resources and clinical management across settings at different care intensity levels.

      Keywords

      1. Introduction

      COVID-19 pandemic is responsible for over 300 million infections worldwide and over five million deaths to date [

      Who Nr 1.Pdf.

      ]. The spectrum of COVID-19 clinical severity is highly heterogeneous and ranges from mild upper respiratory tract symptoms to severe respiratory insufficiency and multiorgan failure [
      Lechien
      Clinical and Epidemiological Characteristics of 14.Pdf.
      ]. Negative health outcomes, including hospitalization and death, have disproportionately affected older people [
      Lechien
      Clinical and Epidemiological Characteristics of 14.Pdf.
      ,
      • Mueller A.L.
      • McNamara M.S.
      • Sinclair D.A.
      Why does COVID-19 disproportionately affect older people?.
      ]. Rising evidence suggests that, besides old age, several risk factors are associated with both COVID-19 severity and progression [
      • Gallo Marin B.
      • Aghagoli G.
      • Lavine K.
      • et al.
      Predictors of COVID ‐19 severity: a literature review.
      ]. In particular, COVID-19 patients with lower functional status, multimorbidity, and those affected by chronic conditions, such as obesity, type 2 diabetes, hypertension, often experienced worse clinical courses, increased length of hospital stay, and mortality [
      • Gallo Marin B.
      • Aghagoli G.
      • Lavine K.
      • et al.
      Predictors of COVID ‐19 severity: a literature review.
      ,
      • Marengoni A.
      • Zucchelli A.
      • Vetrano D.L.
      • et al.
      Beyond chronological age: frailty and multimorbidity predict in-hospital mortality in patients with coronavirus disease 2019.
      ].
      Hospitalized COVID-19 patients show varying disease dynamics [
      • Mody A.
      • Lyons P.G.
      • Vazquez Guillamet C.
      • et al.
      The clinical course of coronavirus disease 2019 in a US hospital system: a multistate analysis.
      ]. While most persons show rapid clinical improvements and require brief stays, others progress towards more severe disease and need admission to intensive care unit (ICU) with or without invasive mechanical ventilation [
      • Mody A.
      • Lyons P.G.
      • Vazquez Guillamet C.
      • et al.
      The clinical course of coronavirus disease 2019 in a US hospital system: a multistate analysis.
      ,
      • Álvarez-Esteban P.C.
      • del Barrio E.
      • Rueda O.M.
      • Rueda C.
      Predicting COVID-19 progression from diagnosis to recovery or death linking primary care and hospital records in Castilla y León (Spain).
      ].
      The risk factors associated with stable/unstable disease courses, as well as worsening or improving transitions, have not been clearly established, because, to date, most of the studies on COVID-19 hospitalization are cross-sectional and focused on critically ill patients, mainly from single clinical centers [
      • Yang X.
      • Yu Y.
      • Xu J.
      • et al.
      Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in Wuhan, China: a single-centered, retrospective, observational study.
      ,
      • Grasselli G.
      • Zangrillo A.
      • Zanella A.
      • et al.
      Baseline characteristics and outcomes of 1591 patients infected with SARS-CoV-2 admitted to ICUs of the Lombardy region, Italy.
      ,
      • Richardson S.
      • Hirsch J.S.
      • Narasimhan M.
      • et al.
      Presenting characteristics, comorbidities, and outcomes among 5700 patients hospitalized with COVID-19 in the New York city Area.
      ]. Indeed, data on transitions between clinical states in COVID-19 hospitalized patients from multiple clinical centers over time are limited [
      • Mody A.
      • Lyons P.G.
      • Vazquez Guillamet C.
      • et al.
      The clinical course of coronavirus disease 2019 in a US hospital system: a multistate analysis.
      ,
      • Álvarez-Esteban P.C.
      • del Barrio E.
      • Rueda O.M.
      • Rueda C.
      Predicting COVID-19 progression from diagnosis to recovery or death linking primary care and hospital records in Castilla y León (Spain).
      ].
      In the present study, we sought to determine the health trajectories of older COVID-19 patients admitted to a network of Italian hospitals. Afterward, we characterized individuals more likely to experience a fluctuating clinical course or health worsening during the hospital stay, who would require closer monitoring in settings at a high intensity of care. This approach may lend important insights into geriatric care needs during the COVID-19 pandemic and provide essential information for guiding clinical decision-making.

      2. Methods

      GeroCovid is a multicenter retrospective-prospective study promoted by the Italian Society of Gerontology and Geriatrics, in collaboration with the Norwegian Society of Gerontology and Geriatrics. GeroCovid was designed to investigate characteristics and clinical outcomes of COVID-19 in persons aged ≥60 years across different healthcare settings. Details on the GeroCovid study protocol can be found elsewhere [
      • Trevisan C.
      • Del Signore S.
      • Fumagalli S.
      • et al.
      Assessing the impact of COVID-19 on the health of geriatric patients: the European GeroCovid Observational Study.
      ].
      In the present study, we included all patients from the Italian GeroCovid Observational – acute wards cohort, admitted with laboratory-confirmed COVID-19 in 19 clinical centers across Italy from March 1 through December 31, 2020.
      The study cohort consisted of 1276 patients. Of these, 252 patients were excluded from the analysis because were younger than <60 years (n = 4) or had incomplete information on clinical status at admission (n = 248). The final study sample included 1024 older patients. Compared with the analytical sample, those excluded due to incomplete data were more likely to be older (80.6 vs 78.1 years, p < 0.001) and not autonomous in living at home (35.9% vs 50.9%, p < 0.001) and in walking (44.8% vs 60.5%, p < 0.001). Instead, no significant differences between groups were found regarding age, sex, and the number and frequency of chronic diseases (data not shown).
      The protocol of the Gerocovid Observational study was reviewed and approved by the Ethics Committee of the Campus Bio-Medico University (Rome, Italy) and was subsequently ratified by the Ethics Committees at all participating centers. Written or dematerialized informed consent was obtained from all patients or their next of kin when applicable before enrolment. In case of impossibility in obtaining the patient's consent, a written declaration was collected by the local investigator, responding to applicable derogations during the COVID-19 pandemic. The study was registered in ClinicalTrials.gov (NCT04379440).

      2.1 Data collection

      Data were collected by trained physicians from hospital and medical records, and, when possible, through interviews with patients or their caregivers. Data were recorded anonymously in an e-Registry developed by Bluecompanion Ltd (London, UK). For the present study, the following information was considered: sociodemographic (i.e., age, sex, ethnicity, and living arrangements) and lifestyle data, functional parameters, medical and medication history, and clinical data (including physical examination, standard blood biochemistry, radiological reports, and oxygen requirement) at hospital admission, during hospital stay until discharge, transfer to other wards, or death.
      Based on smoking habits, participants were classified as current, former, and never smokers. As a proxy of premorbid functional status, information on mobility level before hospitalization, categorized as high (i.e., walking independently or with a single cane) or low (i.e., using a walker, wheelchaired, or bedridden), were considered.
      The presence of chronic diseases was ascertained based on medical records and medication history (for the complete list of chronic diseases please see Appendix 2). The number of chronic diseases was used as an indicator of multimorbidity.
      White blood cell (WBC), lymphocyte (LCT) and platelet (PLT) counts, International Normalized Ratio (INR), Activated Partial Thromboplastin Time (APTT), and serum levels of D-dimer (DD), C-reactive protein (CRP), hemoglobin (Hb) and creatinine were measured using standard biochemistry methods on fully automated testing systems.
      The clinical status of the study participants was recorded in the e-Registry at hospital admission and during the hospitalization in case of changes in the clinical conditions, categorized through the World Health Organization classification [

      COVID-19_Treatment_Trial_Design_Master_Protocol_synopsis_Final_18022020.pdf.

      ] considering the need for low- or high-flow oxygen therapy and organ support. Data on patients' hospital discharge, transfer to ICU or lower-intensity care settings (e.g. long-term care unit, rehabilitation unit), and date of death were also collected at the end of the observation period. Based on this information, we distinguished the following seven states: hospital discharge (state 1), transfer to unspecified or lower-intensity care setting (state 2), mild disease with no need of oxygen therapy (state 3), mild disease with the need for low-flow oxygen therapy (state 4), severe disease with the need for high-flow oxygen therapy or noninvasive ventilation [NIV] (state 5), severe disease with the need for mechanical ventilation and/or organ support or transfer to ICU (state 6), and death (state 7). In order to synthesize the clinical course of the study participants over the hospitalization, from the collected information, we derived the patients’ status at admission (time 0) and after at 3, 5, 10, and 30 days.
      In this study, patients’ health trajectories were explored both by considering all the single transitions between the above-listed states and categorizing the latter into three main clinical courses, i.e., positive, fluctuating, and negative course. The positive clinical course included patients who experienced only transitions from worse to better clinical states, hospital discharge, transfer to low-intensity care settings, or who had a stable mild COVID-19 disease over the observation period. The fluctuating clinical course included patients who experienced both transitions from worse to better, and from better to worse states. The negative clinical course included patients who experienced only transitions from better to worse clinical states or death, or who had a stable severe COVID-19 disease over the observation period.

      2.2 Statistical analysis

      Participant characteristics are described as mean (standard deviation) or median (interquartile range) values for quantitative variables and as counts and percentages for qualitative variables, according to the main clinical courses. Comparisons were performed through ANOVA and Chi-square statistics, as appropriate.
      Transitions between different COVID-19 severity states and outcomes at pre-specified time points are illustrated through an alluvial plot, and the probability of these transitions was estimated using non-hidden continuous-time Markov models. For this analysis, all transitions over the observation period were considered, while death, transfer to other settings, and end of observation were set as absorbing states.
      Sociodemographic (age, sex, living arrangements), time- (COVID-19 wave), and health-related factors (mobility level, number of chronic diseases, CVD, obesity, diabetes, CKD, cognitive disorders, respiratory diseases, depressive mood, and inflammatory and coagulation markers) that were potentially associated with the probability of experiencing transitions were identified using proportional intensity models and expressed as hazard ratio (HR) with 95% confidence intervals (95%CIs) [
      • Mueller A.L.
      • McNamara M.S.
      • Sinclair D.A.
      Why does COVID-19 disproportionately affect older people?.
      ]. For this analysis, we performed a logarithmic transformation of the inflammatory and coagulation markers due to their non-normal distribution. The model convergence was optimized using a quasi-Newton optimization algorithm (the Broyden – Fletcher – Goldfarb – Shanno, BFGS) and a discrete-time model. Factors were tested first in univariable analyses, and then, in analyses adjusted for age and sex. The chance of all possible transitions was evaluated, and participants stable in a given status at each time-point were considered as the reference category.
      In all analyses, a p-value <0.05 was considered statistically significant. Analyses were performed using ggalluvial and msm packages in R.

      3. Results

      The mean age of the 1024 participants was 78.1 (SD 9.3) years, 51.1% were women. More than 80% of the study population was enrolled during the first COVID-19 wave (Table 1). One out of five lived in a nursing home, and more than one-third had a low mobility level. The mean number of chronic diseases was four, with cardiovascular disease (CVD), musculoskeletal disorders, and diabetes being the most common conditions. During the observation period, 637 (62.2%) participants experienced a positive clinical course, while 66 (6.4%) had a fluctuating and 321 (31.3%) had a negative course. Most of the patients with a fluctuating COVID-19 course experienced a transition toward a worse clinical status, followed by an improvement; only for one patient, we recorded three transitions, i.e. worsening, improvement and worsening again. Participants in this group were more likely to be younger, to have better mobility and cognitive levels, fewer diseases, but a high prevalence of CVD and obesity (Table 1). These participants were also more often admitted with a mild disease requiring no or low-flow oxygen therapy. Individuals who experienced a negative clinical course were more likely to be older, men, with low mobility level, and one out of four lived in a nursing home. These participants had the highest number of chronic diseases, especially CVD, cerebrovascular and cognitive disorders, and most of them had low- (43%) or high-flow (37.3%) oxygen requirements. As for COVID-19 symptoms and biochemical parameters at ward admission, participants with a negative clinical course presented more frequently with typical (e.g. fever, cough, dyspnea) and some atypical symptoms (delirium), and had higher serum creatinine values (Supplementary Table 1).
      Table 1Characteristics of the study population according to the main clinical course during the observation period.
      NAll (n = 1024)Clinical coursep-value
      Positive (n = 637)Fluctuating (n = 66)Negative (n = 321)
      Age (years)78.07 (9.32)77.25 (9.36)72.29 (8.55)80.88 (8.51)<0.001
      Women513 (50.1)342 (53.7)28 (42.4)143 (44.5)0.012
      Living arrangement
      Missing values in living arrangements (n = 118), mobility level (n = 31), smoking habits (n = 482). P-values refer to the comparison between different categories of clinical trajectory.
      <0.001
       At home, alone521 (50.9)355 (55.7)52 (78.8)114 (35.5)
       At home, assisted196 (19.1)113 (17.7)3 (4.5)80 (24.9)
       Institutionalized189 (18.5)111 (17.4)3 (4.5)75 (23.4)
      Low mobility level
      Missing values in living arrangements (n = 118), mobility level (n = 31), smoking habits (n = 482). P-values refer to the comparison between different categories of clinical trajectory.
      373 (36.4)206 (32.3)4 (6.1)163 (50.8)<0.001
      Smoking habits
      Missing values in living arrangements (n = 118), mobility level (n = 31), smoking habits (n = 482). P-values refer to the comparison between different categories of clinical trajectory.
      0.027
       Never367 (35.8)222 (34.9)31 (47.0)114 (35.5)
       Former149 (14.6)83 (13.0)14 (21.2)52 (16.2)
       Current26 (2.5)14 (2.2)3 (4.5)9 (2.8)
      Covid-19 wave II126 (12.3)76 (11.9)16 (24.2)34 (10.6)0.008
      N. chronic diseases4.06 (2.66)3.88 (2.65)3.50 (2.38)4.53 (2.67)<0.001
      Diabetes mellitus258 (25.2)163 (25.6)17 (25.8)78 (24.3)0.905
      Chronic liver diseases24 (2.3)14 (2.2)3 (4.5)7 (2.2)0.474
      Osteoporosis277 (27.1)181 (28.4)8 (12.1)88 (27.4)0.018
      Osteoarthrosis219 (21.4)141 (22.1)7 (10.6)71 (22.1)0.087
      Hypertension694 (67.8)425 (66.7)45 (68.2)224 (69.8)0.631
      CVD590 (57.6)338 (53.1)42 (63.6)210 (65.4)0.001
      Cerebrovascular diseases111 (10.8)59 (9.3)4 (6.1)48 (15.0)0.012
      Chronic respiratory diseases146 (14.3)87 (13.7)8 (12.1)51 (15.9)0.568
      CKD135 (13.2)79 (12.4)3 (4.5)53 (16.5)0.021
      Depressive disorders181 (17.7)114 (17.9)11 (16.7)56 (17.4)0.961
      Cognitive disorders164 (16.0)100 (15.7)1 (1.5)63 (19.6)0.001
      Obesity153 (14.9)82 (12.9)17 (25.8)54 (16.8)0.01
      WHO status at ward admission<0.001
       Mild disease, no O2303 (29.6)232 (36.4)23 (34.8)48 (15.0)
       Mild disease, low-flow O2445 (43.5)270 (42.4)38 (57.6)137 (42.7)
       Severe disease, high-flow O2/NIV248 (24.2)117 (18.4)5 (7.6)126 (39.3)
       Severe disease, organ support28 (2.7)18 (2.8)0 (0.0)10 (3.1)
      Abbreviations: CVD, cardiovascular diseases; CKD, chronic kidney disease; WHO, World Health Organization.
      a Missing values in living arrangements (n = 118), mobility level (n = 31), smoking habits (n = 482). P-values refer to the comparison between different categories of clinical trajectory.
      When looking at all the transitions between the single COVID-19 states (Fig. 1 and Supplementary Table 2), we found that the mean estimated permanence time in each state was consistently around 3 days (Supplementary Table 3). Table 2 shows the probabilities of experiencing such transitions estimated through multistate Markov models. As reported, the probability of experiencing clinical transitions during the hospitalization was higher within a 10-day time frame, especially for the less severe states.
      Fig. 1
      Fig. 1Alluvial plot for the transitions of older patients with COVID-19 between different clinical states since hospital admission (time 0).
      Table 2Transition's probabilities at 3, 5, 10, and 30 days according to participants' clinical status.
      From3-day transition's probability (%) to
      State 1State 2State 3State 4State 5State 6State 7
      State 334.49.542.23.72.91.36.1
      State 418.96.811.739.99.22.311.3
      State 515.08.65.93.334.93.428.8
      State 620.67.33.46.41.536.124.7
      5-day transition's probability (%) to
      State 346.212.924.23.52.81.39
      State 428.510.211.222.38.22.317.3
      State 521.311.65.63.217.72.937.6
      State 628.19.93.75.71.818.532.4
      10-day transition's probability (%) to
      State 359.416.96.51.81.50.713.3
      State 442.415.15.75.73.61.326
      State 529.315.12.81.63.61.246.3
      State 636.9132.32.51.23.640.4
      30-day transition's probability (%) to
      State 365.218.70.100015.9
      State 450.817.90.10.10031.1
      State 533.716.6000049.6
      State 641.614.60.100043.7
      Notes. State 1, hospital discharge with clinical improvement/stability; state 2, transfer to unspecified or low-intensity of care setting; state 3, mild disease – no O2-therapy; state 4, mild disease – low-flow O2-therapy; state 5, severe disease – high-flow O2-therapy or NIV; state 6, severe disease – intubation/organ support/ICU transfer; state 7, death.
      In particular, mild COVID-19 patients with no oxygen therapy had a chance of requiring low- or high-flow oxygen therapy at 3 days of 3.7% and 2.9%, respectively, while the probability of transition towards ICU admission was 1.3%. Considering the health trajectories of patients with some oxygen requirements, we found that those with mild COVID-19 and low-flow oxygen therapy had a 9.2% chance at 3 days of worsening towards the need for higher oxygen support or NIV. For these patients, as well as for those with severe COVID-19 disease and higher oxygen requirements, the probability of ICU admission over the entire observation period remained below 4%. Concerning mortality, the 30-day probability of death ranged from 16% for those who did not require oxygen therapy to 49.6% for those who needed high-flow oxygen or NIV, and 43.7% for those who were mechanically ventilated or required organ support. Conversely, the 30-day chance of being discharged was 65.2% for those with mild disease and no oxygen requirements, and 33.7% and 41.6% for those in the most severe COVID-19 categories (state 5 and 6).
      When identifying the factors related to the above-described transitions, we found that older age, low mobility level, living in a nursing home, former smoking, a greater number of chronic diseases, CVD, cognitive disorders, and depressive mood made the individuals less likely to reverse from worse to milder clinical states (Table 3, Table 4; for the univariable analyses, please see Supplementary Tables 4 and 5). Female sex decreased the probability of worsening from mild COVID-19 states, while the opposite trend was observed for those who had fever at admission and for current smokers. The probability of death increased with age, male sex, low mobility level, living at home with the need of assistance, being institutionalized, and having a greater number of chronic diseases. Individuals who were hospitalized in the COVID-19 wave II compared with wave I had an increased probability of improving and being discharged from mild or severe COVID-19 status requiring oxygen therapy in wave II than in wave I. An opposite trend was observed for the transition from mild COVID-19 no requiring oxygen to mild COVID-19 requiring low-flow oxygen, which was experienced by only a few patients (n = 22, Supplementary Table 2). Instead, after adjusting for age and sex, no significant results were found concerning some chronic conditions, such as obesity and respiratory and cognitive disorders. Finally, among biochemical parameters (Supplementary Tables 4–7), higher lymphocytes were linked to higher chances of improvements from mild COVID-19 states, while higher platelet, D-dimer (only for severe COVID-19 states), CRP, and creatinine levels were associated with a lower probability of reversing to milder clinical states or with increased mortality.
      Table 3Factors associated with transitions from mild disease – no O2-therapy and COVID-19 status (age- and sex-adjusted model).
      Hazard Ratios (95% Confidence Intervals) of Transition
      From state 3 (mild disease no O2-therapy), toFrom state 4 (mild disease low-flow O2-therapy), to
      State 1State 2State 4State 5State 6State 7State 1State 2State 3State 5State 6State 7
      Age (years)0.98 (0.96– 0.99)1.02 (0.98–1.06)0.97 (0.92–1.02)0.96 (0.9–1.02)0.94 (0.85–1.03)1.06 (1.01–1.13)0.98 (0.96–1.00)1.00 (0.96–1.04)0.97 (0.94–0.99)0.97 (0.95–1.00)0.97 (0.91–1.03)1.05 (1.02–1.09)
      Sex (F vs M)0.72 (0.51–1.01)0.85 (0.44–1.66)0.35 (0.13–0.91)0.38 (0.12–1.25)0.37 (0.06–2.32)0.99 (0.38–2.59)0.89 (0.59–1.35)1.51 (0.72–3.17)1.17 (0.76–1.82)0.56 (0.35–0.91)0.40 (0.12–1.32)1.27 (0.68–2.37)
      Not walk independently0.76 (0.50–1.15)2.74 (1.26–5.97)0.97 (0.33–2.82)0.72 (0.07–7.22)1.49 (0.58–3.79)0.64 (0.41–0.99)1.77 (0.89–3.55)0.60 (0.36–0.99)0.68 (0.39–1.17)0.10 (0.01–0.79)4.52 (2.34–8.77)
      Lives at home autonomous[ref][ref][ref][ref][ref][ref][ref][ref][ref][ref][ref][ref]
      Lives at home dependent0.66 (0.39–1.09)1.70 (0.69–4.22)1.08 (0.32–3.63)0.21 (0.03–1.69)1.52 (0.14–16.1)0.97 (0.23–4.17)0.63 (0.36–1.08)1.17 (0.45–3.08)0.84 (0.49–1.44)0.84 (0.46–1.56)0.35 (0.07–1.67)2.73 (1.25–5.95)
      Lives in NH0.69 (0.41–1.16)3.05 (1.31–7.09)0.31 (0.04–2.56)4.28 (1.40–13.2)0.78 (0.45–1.37)3.04 (1.32–7.02)0.34 (0.15–0.75)0.40 (0.16–0.99)0.23 (0.03–1.94)4.12 (1.87–9.06)
      Smoking habit (never)[ref][ref][ref][ref][ref][ref][ref][ref][ref][ref][ref][ref]
      Former1.78 (1.04–3.04)0.45 (0.10–2.00)0.78 (0.23–2.68)0.39 (0.08–1.91)1.82 (0.32–10.5)0.80 (0.16–3.85)0.59 (0.30–1.14)1.84 (0.75–4.51)0.96 (0.44–2.07)1.09 (0.59–2.02)2.21 (0.61–7.99)1.28 (0.58–2.82)
      Current2.12 (0.65–6.89)1.61 (0.20–12.9)1.75 (0.21–14.6)0.40 (0.05–3.0)2.79 (0.96–8.1)8.13 (1.49–44.4)1.78 (0.23–13.5)
      N. diseases0.94 (0.88–1.01)1.00 (0.89–1.12)1.01 (0.86–1.18)0.96 (0.79–1.16)0.90 (0.65–1.23)0.99 (0.85–1.15)0.97 (0.90–1.05)0.96 (0.85–1.09)0.92 (0.84–1.00)1.02 (0.94–1.12)0.87 (0.69–1.1)1.07 (0.98–1.16)
      Obesity1.07 (0.69–1.65)0.75 (0.22–2.56)0.66 (0.15–2.95)6.05 (1.33–27.6)0.65 (0.23–1.83)0.58 (0.13–2.47)1.11 (0.68–1.81)1.08 (0.63–1.87)2.45 (1.48–4.06)2.11 (0.67–6.70)0.26 (0.06–1.07)0.59 (0.25–1.38)
      CVD0.71 (0.51–0.98)0.58 (0.31–1.1)0.99 (0.42–2.35)1.52 (0.53–4.31)6.3 (0.76–52.3)1.75 (0.72–4.26)1.30 (0.89–1.90)0.71 (0.38–1.30)0.63 (0.41–0.96)1.23 (0.77–1.97)0.96 (0.35–2.61)1.36 (0.81–2.29)
      CKD0.92 (0.58–1.45)0.62 (0.24–1.6)0.71 (0.2–2.46)2.37 (0.78–7.21)2.41 (1.05–5.57)0.94 (0.53–1.67)2.63 (1.32–5.24)0.51 (0.22–1.18)0.50 (0.20–1.24)0.97 (0.50–1.85)
      Respiratory diseases1.23 (0.78–1.95)0.32 (0.04–2.60)1.05 (0.24–4.62)1.06 (0.42–2.69)0.69 (0.17–2.90)0.69 (0.40–1.21)0.96 (0.54–1.70)1.29 (0.73–2.26)0.78 (0.18–3.40)0.38 (0.11–1.25)1.07 (0.57–1.98)
      Cognitive disorders0.77 (0.46–1.29)1.23 (0.57–2.67)0.63 (0.14–2.88)0.66 (0.24–1.85)0.85 (0.50–1.46)0.96 (0.43–2.13)0.47 (0.22–0.98)0.47 (0.20–1.11)0.93 (0.20–4.37)1.32 (0.77–2.26)
      Depressive mood0.77 (0.50–1.20)0.86 (0.4–1.85)1.06 (0.35–3.21)0.69 (0.15–3.13)0.94 (0.11–7.96)0.83 (0.32–2.14)1.05 (0.65–1.7)1.86 (0.95–3.64)0.49 (0.24–0.98)0.73 (0.36–1.49)0.89 (0.19–4.04)1.45 (0.85–2.49)
      Fever
      Symptoms at ward admission.
      1.07 (0.73–1.58)0.97 (0.46–2.07)1.02 (0.39–2.7)2.57 (0.57–11.7)0.63 (0.25–1.61)0.68 (0.44–1.05)1.91 (0.66–5.50)1.01 (0.54–1.86)3.84 (1.39–10.6)3.17 (0.40–25.3)0.83 (0.47–1.47)
      Delirium
      Symptoms at ward admission.
      14.64 (1.66–129)0.69 (0.33–1.45)0.91 (0.26–3.15)0.72 (0.25–2.06)0.88 (0.37–2.1)0.66 (0.08–5.44)1.54 (0.77–3.09)
      Wave II vs I1.23 (0.73–2.06)0.53 (0.13–2.21)3.11 (1.14–8.46)2.39 (0.68–8.42)0.93 (0.22–3.96)0.79 (0.47–1.33)0.24 (0.06–0.99)1.84 (1.16–2.92)1.34 (0.78–2.30)0.32 (0.04–2.38)0.87 (0.46–1.65)
      Abbreviations: CVD, cardiovascular diseases; CKD, chronic kidney disease; NH, nursing home Notes. State 1, hospital discharge with clinical improvement/stability; state 2, transfer to unspecified or low-intensity of care setting; state 3, mild disease – no O2-therapy; state 4, mild disease – low-flow O2-therapy; state 5, severe disease – high-flow O2-therapy or NIV; state 6, severe disease – intubation/organ support/ICU transfer; state 7, death.
      a Symptoms at ward admission.
      Table 4Factors associated with transitions from severe COVID-19 (age- and sex-adjusted model).
      Hazard Ratios (95% Confidence Intervals) of Transition
      From state 5 (severe disease – high-flow O2-therapy or NIV), toFrom state 6 (severe disease – intubation/organ support/ICU), to
      State 1State 2State 3State 4State 6State 7State 1State 2State 3State 4State 5State 7
      Age (years)0.97 (0.94–1.00)0.99 (0.95–1.03)0.94 (0.90–0.98)0.91 (0.85–0.97)0.94 (0.89–1.00)1.09 (1.06–1.11)1.01 (0.93–1.09)0.98 (0.86–1.11)1.01 (0.87–1.16)0.94 (0.84–1.06)1.13 (0.88–1.45)1.05 (0.99–1.12)
      Sex (F vs M)1.70 (0.96–3.01)0.81 (0.39–1.68)1.08 (0.50–2.32)1.04 (0.36–2.97)0.63 (0.22–1.84)0.56 (0.38–0.82)1.98 (0.64–6.09)0.59 (0.07–4.97)0.02 (0–882.3)1.37 (0.30–6.22)0.23 (0.05–1.07)
      Not walk independently0.49 (0.23–1.07)1.28 (0.56–2.92)0.26 (0.06–1.14)2.47 (0.82–7.45)1.90 (1.25–2.89)3.17 (0.66–15.1)
      Lives at home autonomous[ref][ref][ref][ref][ref][ref][ref][ref][ref][ref][ref][ref]
      Lives at home with assistance0.39 (0.14–1.09)0.74 (0.25–2.20)0.62 (0.18–2.10)1.76 (0.48–6.49)0.32 (0.04–2.42)2.60 (1.63–4.16)0.34 (0.04–2.81)4.05 (0.21–77.2)6.18 (0.97–39.3)
      Lives in NH0.88 (0.36–2.14)1.08 (0.33–3.51)0.34 (0.04–2.65)1.23 (0.15–10.1)1.86 (1.03–3.34)12.27 (0.91–166.2)
      Smoking habit (never)[ref][ref][ref][ref][ref][ref][ref][ref][ref][ref][ref][ref]
      Former0.71 (0.34–1.48)1.04 (0.46–2.35)1.20 (0.46–3.14)1.11 (0.32–3.84)1.14 (0.27–4.83)0.91 (0.53–1.55)1.47 (0.29–7.45)5.01 (0.31–81.9)1.26 (0.17–9.46)0.72 (0.18–2.89)
      Current0.76 (0.10–5.83)1.18 (0.15–9.37)9.69 (2.47–38.1)1.14 (0.35–3.74)4.05 (1.03–15.9)0.39 (0.05–3.14)
      N. chronic diseases0.94 (0.84–1.05)1.07 (0.93–1.22)1.04 (0.89–1.22)0.85 (0.67–1.09)1.04 (0.85–1.28)1.10 (1.03–1.18)1.15 (0.90–1.46)0.85 (0.52–1.38)0.66 (0.33–1.36)1.20 (0.82–1.78)1.68 (0.48–5.84)0.93 (0.74–1.16)
      Obesity1.19 (0.64–2.21)0.47 (0.14–1.55)1.25 (0.41–3.82)2.74 (1.07–7.01)0.28 (0.07–1.18)1.43 (0.91–2.25)1.35 (0.36–5.01)5.13 (0.94–28.0)1.94 (0.71–5.27)
      CVD1.10 (0.65–1.85)1.28 (0.64–2.55)1.66 (0.78–3.51)0.42 (0.14–1.22)1.20 (0.47–3.07)1.19 (0.81–1.75)0.91 (0.30–2.72)1.95 (0.23–17.0)3.22 (0.38–26.9)0.72 (0.27–1.90)
      CKD0.38 (0.09–1.59)2.08 (0.84–5.20)1.61 (0.47–5.58)1.11 (0.14–9.09)1.36 (0.85–2.20)1.67 (0.18–15.2)
      Respiratory diseases1.20 (0.59–2.46)2.01 (0.84–4.80)0.36 (0.04–3.07)1.67 (0.54–5.23)1.11 (0.46–2.69)0.86 (0.51–1.45)1.87 (0.59–5.98)1.28 (0.42–3.88)
      Cognitive disorders1.20 (0.49–2.94)0.54 (0.12–2.38)2.64 (0.58–12.0)1.32 (0.81–2.15)0.74 (0.09–6.36)1.14 (0.13–9.90)
      Depressive mood0.86 (0.41–1.77)1.81 (0.83–3.94)0.65 (0.20–2.14)1.35 (0.38–4.79)1.04 (0.65–1.68)2.87 (0.37–22.3)5.40 (0.62–46.8)
      Fever
      Symptoms at ward admission.
      5.17 (1.23–21.7)1.88 (0.57–6.19)1.70 (0.52–5.54)0.61 (0.17–2.21)0.48 (0.17–1.36)0.68 (0.44–1.05)0.72 (0.17–3.08)0.60 (0.04–8.11)0.84 (0.08–9.42)1.99 (0.39–10.33)
      Delirium
      Symptoms at ward admission.
      0.89 (0.38–2.13)0.62 (0.14–2.69)0.89 (0.27–3.00)0.79 (0.10–6.15)1.10 (0.68–1.76)0.41 (0.05–3.33)4.15 (0.25–70.2)1.38 (0.36–5.31)
      Wave II vs I2.79 (1.56–4.97)0.49 (0.12–2.03)0.77 (0.23–2.55)1.95 (0.63–6.00)0.67 (0.33–1.37)5.78 (0.66–50.9)
      Abbreviations: CVD, cardiovascular diseases; CKD, chronic kidney disease; NH, nursing home. Notes. State 1, hospital discharge with clinical improvement/stability; state 2, transfer to unspecified or low-intensity of care setting; state 3, mild disease – no O2-therapy; state 4, mild disease – low-flow O2-therapy; state 5, severe disease – high-flow O2-therapy or NIV; state 6, severe disease – intubation/organ support/ICU transfer; state 7, death.
      a Symptoms at ward admission.

      4. Discussion

      The present study found that COVID-19 has a heterogeneous and dynamic clinical course in hospitalized older patients. Health transitions occurred over a short timeframe, especially during the first 10 days of admission and even the mildest clinical states showed to be stable for no longer than 3 days before progressing toward better or worse conditions.
      The above findings are in keeping with those obtained from a large cohort of patients hospitalized with COVID-19 in a US hospital network, where both the risk of decompensation and discharge peaked in the first 3–5 days after admission [
      • Mody A.
      • Lyons P.G.
      • Vazquez Guillamet C.
      • et al.
      The clinical course of coronavirus disease 2019 in a US hospital system: a multistate analysis.
      ]. These data underline the importance of closely monitoring older patients with COVID-19 during the first days of hospitalization, also in cases with mild disease.
      Notably, in our study population, around one out of ten individuals who required no or low-flow oxygen therapy worsened towards more severe clinical states. However, the probability of progressing towards intensive care unit admission or organ support was low and did not reach 4% even among patients who presented with severe disease needing high-flow oxygen therapy or NIV. This result differs from previous studies in younger patients, in whom greater COVID-19 severity at admission was associated with a higher probability of ICU transfer [
      • Pijls B.G.
      • Jolani S.
      • Atherley A.
      • et al.
      Demographic risk factors for COVID-19 infection, severity, ICU admission and death: a meta-analysis of 59 studies.
      ,
      • Cecconi M.
      • Piovani D.
      • Brunetta E.
      • et al.
      Early predictors of clinical deterioration in a cohort of 239 patients hospitalized for covid-19 infection in Lombardy, Italy.
      ,
      • Swearingen D.
      • Boverman G.
      • Tgavalekos K.
      • et al.
      A retrospective cohort study of clinical factors associated with transitions of care among COVID-19 patients.
      ,
      • Booth A.
      • Reed A.B.
      • Ponzo S.
      • et al.
      Population risk factors for severe disease and mortality in COVID-19: a global systematic review and meta-analysis.
      ]. Other studies and meta-analyses showed that the progressive age-related increase in in-hospital mortality was not associated with a similar trend in ICU admission, which, instead, tended to decrease in the oldest old [

      NR 34 ciaa1012.Pdf.

      ,
      • Cohen J.F.
      • Korevaar D.A.
      • Matczak S.
      • Chalumeau M.
      • Allali S.
      • Toubiana J.
      COVID-19–Related fatalities and intensive-care-unit admissions by age groups in Europe: a meta-analysis.
      ,
      • Bergman J.
      • Ballin M.
      • Nordström A.
      • Nordström P.
      Risk factors for COVID-19 diagnosis, hospitalization, and subsequent all-cause mortality in Sweden: a nationwide study.
      ,
      • Bennett K.E.
      • Mullooly M.
      • O'Loughlin M.
      • et al.
      Underlying conditions and risk of hospitalisation, ICU admission and mortality among those with COVID-19 in Ireland: a national surveillance study.
      ]. Collectively, these results suggest that older adults might have been excluded from more intensive care, especially during the first pandemic waves when there was limited healthcare resource availability and a shortage of ICU beds [
      • Álvarez-Esteban P.C.
      • del Barrio E.
      • Rueda O.M.
      • Rueda C.
      Predicting COVID-19 progression from diagnosis to recovery or death linking primary care and hospital records in Castilla y León (Spain).
      ,
      • Trevisan C.
      • Pedone C.
      • Maggi S.
      • et al.
      Accessibility to SARS-CoV-2 swab test during the Covid-19 pandemic: did age make the difference?.
      ]. Accordingly, patients who were hospitalized during pandemic wave II were more likely to transition towards milder COVID-19 and had a higher probability of recovering from more severe disease states [
      • Mody A.
      • Lyons P.G.
      • Vazquez Guillamet C.
      • et al.
      The clinical course of coronavirus disease 2019 in a US hospital system: a multistate analysis.
      ].
      Concerning the COVID-19 course, we found that half of the sample experienced a positive course with a progressive improvement in clinical status, while almost one-third showed a gradual worsening. In keeping with previous studies [
      • Marengoni A.
      • Zucchelli A.
      • Vetrano D.L.
      • et al.
      Beyond chronological age: frailty and multimorbidity predict in-hospital mortality in patients with coronavirus disease 2019.
      ,
      • Mody A.
      • Lyons P.G.
      • Vazquez Guillamet C.
      • et al.
      The clinical course of coronavirus disease 2019 in a US hospital system: a multistate analysis.
      ,
      • Álvarez-Esteban P.C.
      • del Barrio E.
      • Rueda O.M.
      • Rueda C.
      Predicting COVID-19 progression from diagnosis to recovery or death linking primary care and hospital records in Castilla y León (Spain).
      ,
      • Swearingen D.
      • Boverman G.
      • Tgavalekos K.
      • et al.
      A retrospective cohort study of clinical factors associated with transitions of care among COVID-19 patients.
      ,
      • Booth A.
      • Reed A.B.
      • Ponzo S.
      • et al.
      Population risk factors for severe disease and mortality in COVID-19: a global systematic review and meta-analysis.
      ,
      • Semenzato L.
      Chronic Diseases, Health Conditions and Risk of COVID-19-Related Hospitalization and In-Hospital Mortality during the First Wave of the Epidemic in France: a Cohort Study of 66 Million People.
      ], patients with a worse clinical course were more likely to be older, to have lower mobility levels and a higher number of chronic conditions. Interestingly, 6.4% of the sample showed a fluctuating course with both worsening and improving clinical changes during hospitalization. These individuals had a generally healthier profile, in terms of sociodemographic data, mobility, cognitive status, and chronic conditions, and were more likely to present with no or low oxygen requirements at ward admission. We can therefore speculate that the fluctuating trend of these patients was due to a more intensive care approach adopted in light of their healthier pre-admission status, or the onset of concurrent acute conditions during the hospital stay. Although we had no available data to verify these hypotheses, the identification of individuals more likely to experience an unstable clinical course is highly relevant to appropriately address patients to care settings with different possibilities of monitoring and intensity of care. In this regard, our study, using advanced statistical analyses, allowed us to characterize the older individuals who had a higher chance of worsening or improving from a certain level of COVID-19 severity. These insights may help to better predict patients’ health trajectories and anticipate their assistance and care needs. In particular, among the factors associated with either a higher probability of experiencing worsening transitions or a lower chance of recovering from COVID-19, we identified older age, male sex, lower mobility level, being institutionalized, and reporting former or current smoking habits. Concerning chronic diseases, multimorbidity and specific chronic conditions were associated with worsening transitions, while fever at admission was identified as a negative prognostic factor. Some other diseases, such as obesity, cognitive and respiratory disorders were not associated with clinical changes irrespective of age and sex. Instead, biochemical parameters indicating higher inflammation, lower lymphocytes count, and worse kidney function were linked to clinical worsening.
      Most of the factors listed above were also associated with a higher risk of in-hospital mortality and corroborate the results of previous studies. Indeed, in addition to demographic factors, laboratory parameters, and vital signs, specific conditions at admission, such as fever and delirium, have previously been related to greater mortality in COVID-19 patients. Poor functional status, which was defined as self-reported low mobility or nursing-home residency, seemed to reduce the chance of COVID-19 recovery, and increase in-hospital mortality in our sample. This point also confirmed studies that considered the impact of dependency in activities of daily living and frailty on COVID-19 outcomes [
      • Semenzato L.
      Chronic Diseases, Health Conditions and Risk of COVID-19-Related Hospitalization and In-Hospital Mortality during the First Wave of the Epidemic in France: a Cohort Study of 66 Million People.
      ,
      • Panagiotou O.A.
      • Kosar C.M.
      • White E.M.
      • et al.
      Risk factors associated with all-cause 30-day mortality in nursing home residents with COVID-19.
      ,
      • Hägg S.
      • Jylhävä J.
      • Wang Y.
      • et al.
      Age, frailty, and comorbidity as prognostic factors for short-term outcomes in patients with coronavirus disease 2019 in geriatric care.
      ]. Consistent with existing literature, a higher number of chronic diseases and some specific conditions, such as cerebrovascular and cardiovascular diseases, were found to be negative prognostic factors in our study [
      • Swearingen D.
      • Boverman G.
      • Tgavalekos K.
      • et al.
      A retrospective cohort study of clinical factors associated with transitions of care among COVID-19 patients.
      ,
      • Kaeuffer C.
      • Le Hyaric C.
      • Fabacher T.
      • et al.
      Clinical characteristics and risk factors associated with severe COVID-19: prospective analysis of 1,045 hospitalised cases in North-Eastern France, March 2020.
      ]. In addition, depressive mood was associated with a lower chance of reverting from worse to better clinical states, in line with the pooled results of a recent meta-analysis that showed higher mortality among COVID-19 patients with mental disorders, including depression [
      • Fond G.
      • Nemani K.
      • Etchecopar-Etchart D.
      • et al.
      Association between mental health disorders and mortality among patients with COVID-19 in 7 countries: a systematic review and meta-analysis.
      ]. ì Similar findings emerged for cognitive disorders both in the hospital and in nursing home settings, which may predispose individuals to COVID-19 clinical complications [
      • Panagiotou O.A.
      • Kosar C.M.
      • White E.M.
      • et al.
      Risk factors associated with all-cause 30-day mortality in nursing home residents with COVID-19.
      ,
      • Batty G.D.
      • Deary I.J.
      • Gale C.R.
      Pre-pandemic cognitive function and COVID-19 mortality: prospective cohort study.
      ].
      This study has limitations and strengths. First, most of the data were collected retrospectively during the most burdensome phases of the pandemic. This, together with the limited human resources dedicated to data collection, may have affected the completeness of the information recorded, especially biochemical and radiologic data. Second, no information was available on the clinical course of patients transferred to institutions outside the GeroCovid network. Third, to explore the probability of different clinical transitions, we derived the patients’ status at specific time points, which allowed us to synthesize the studied phenomenon and facilitated the multistate analysis but could have partly limited our evaluation of the disease course variability. Finally, data for this study refer to the COVID-19 course during the first pandemic waves; therefore, similar analyses should be performed considering the disease caused by the most recent virus variants. Concerning the study strengths, the multicenter nature of the GeroCovid allowed collecting information on older patients hospitalized across Italy. Moreover, the large cohort involved and the number of health-related parameters evaluated allowed obtaining a robust and comprehensive picture of the clinical course of COVID-19 in older patients. Finally, to our knowledge, this is the first study investigating the health trajectories of older patients with COVID-19 using both classical and advanced statistical approaches.
      In conclusion, COVID-19 in hospitalized older adults may present a complex and dynamic clinical course characterized by worsenings and improvements within short timeframes, i.e. 3-10 days. The identification of individuals with a more fluctuating clinical course and higher probability of worsening may help predict outcomes to better organize hospital pathways and healthcare resources allocation for older patients with COVID-19.

      Author agreement

      We declare that the manuscript is original, has not been published before and is not currently being considered for publication elsewhere.
      E confirm that the manuscript has been read and approves by all named authors and that there are no other person who satisfied the criteria for authorship but are not listed. We further confirm that the order of author listed in the manuscript has been approved by all of the authors.
      We understand that the Corresponding Author is the contact for Editorial process. He/She is responsible for communicating the authors about progress, submission of revision and final approval of proofs.

      Funding sources

      This research did not receive any funding from agencies in the public, commercial, or not-for-profit sectors.

      Declaration of competing interest

      Authors declare no conflict of interests for this article.

      Acknowledgements

      We are thankful to our colleagues who are collaborating in data collection for their valuable contribution, and to all the study participants. We thank Gilda Borselli for her precious support for the organization of the GeroCovid initiative.

      Appendix A. Supplementary data

      The following is the Supplementary data to this article:

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